Finding new pulsars has always been a challenging problem, but this challenge is nowadays exacerbated by the increasing data rates of modern radio telescopes. Because of these increased data rates, traditional approaches to searching, based on storing data for off-line processing, are becoming unfeasible. Therefore, we propose a new pulsar searching pipeline that, by exploiting high-performance computing techniques, is able to process observational data in real-time. To achieve the real-time goal we parallelized all the steps of the pipeline to run on many-core accelerators, and used auto-tuning to adapt and optimize the pipeline for different platforms, telescopes, and searching parameters. In this paper, we test our pipeline on three different platforms: two Graphics Processing Units from AMD and NVIDIA, and an Intel Xeon Phi. Furthermore, we test it on three different scenarios, based on the operational parameters of three state-of-the-art telescopes. Results show that our pipeline can adapt to all tested platforms and scenarios, and achieves real-time performance and linear scalability. Because power consumption is a main concern for radio telescopes, and will be the main bottleneck for the construction of the Square Kilometer Array, we also measure the power consumed by our pipeline. By comparing the results obtained on many-core accelerators with the results obtained using a traditional multi-core CPU, we conclude that the accelerators can provide up to a factor 8 improvement in execution time, and up to a factor 6 reduction in power consumption.